Pierfranco Costabile

Sediment delivery refers to the ensemble of processes connecting on-site soil erosion within a catchment to the off-site sediment yield measured at the river basin outlet (Walling 1983). Sediment d...

This study presents a practical application of a subgrid approach for urban pluvial flooding that implicitly accounts for the secondary drainage network. Parameters required by the subgrid model (e.g., pipe diameter, spacing) can be easily estimated, reducing the data requirements and modelling efforts compared to classical 1D/2D simulations. Applied to simulate the 2009 flooding of a Padova district, the model improves accuracy in flood extent and water levels compared to models that ignore the secondary network, without the need for surveys of smaller-scale pipes. This data-efficient approach proves to be a practical tool for simulating urban pluvial floods, particularly in data-scarce urban areas.

Flash floods frequently occur in steep mountainous catchments, where topographic gradients play a crucial role in concentrating and accelerating runoff processes. Two‐dimensional (2D) shallow water equations (SWE) are widely used to simulate such events; however, these equations are classically derived assuming small bottom slopes, typically less than 10%. This assumption, seldom questioned, may introduce inaccuracies when SWE models are applied to mountain watersheds, where slopes commonly exceed the theoretical range of validity. This study addresses this inconsistency by revisiting the theoretical foundations of SWE‐based modeling and extending the recently proposed 2D Steep‐Slope Shallow Water Equations (SSSWE) to overland flow. The formulation explicitly accounts for steep‐slope geometric effects, offering a more physically consistent description of flow dynamics in mountainous terrain. A kinematic‐wave‐based reference framework in one‐ and two‐dimensional domains is presented to quantify errors arising from the use of conventional SWE under the small‐angle approximation on steep slopes. Results from idealized test cases (including a V‐shaped catchment) show that the standard SWE predicts lower equilibrium water depths and response times and higher flow velocities than the improved steep‐slope model. These discrepancies become substantial for slopes steeper than 20°. To incorporate steep‐slope effects without modifying existing SWE‐based numerical solvers, an analytically derived, slope‐dependent adjustment to the Manning roughness coefficient is proposed; in 2D, the correction is anisotropic. Overall, this study provides the first systematic analytical assessment of inaccuracies in SWE‐based flash‐flood modeling for steep watersheds, addressing a long‐standing theoretical gap and improving the physical realism of hydrodynamic models in mountainous environments.

Flood nowcasting at detailed, fine spatiotemporal scales is crucial for the deployment of reliable warning systems, especially in built-up environments where the majority of socio-economic activity is concentrated. These environments are also characterized by significant complexities that require sufficient detail, up to street level. The derivation of flood maps for early warning systems can be organized via three main pillars: a) nowcasting of rainfall at high spatiotemporal resolution, typically obtained from weather radars; b) deployment of physics-based mechanistic simulators, typically based on 2D Shallow Water Equations; c) utilization of High-Performance Computing (HPC) facilities to handle the associated significant computational effort and make practically feasible the computational process. However, even with such infrastructure, there are still limitations mainly arising from: a) model errors, either related with the epistemic or the deep uncertainty of real-world randomness; b) the required simulation time which can still be prohibitive for the development of operational nowcasting tools, especially for large case study areas. The first limitation is addressed through impact-based approaches, in which uncertainties are compensated through the translation of the natural variables derived by the model (i.e. water depths and flow velocities) into classified hazard zones. With respect to the second limitation, surrogate modelling, and particularly the relevant Machine Learning (ML) techniques, promises a potential remedy to the high computational burden, since it enables the development of fast emulators based on the results derived by the mechanistic (accurate, yet slow) simulators. However, the high spatiotemporal variability of flood-related variables, as exhibited in detailed scales increases significantly the dimensionality of the problem, hampering the application of such techniques in real-world operational conditions. To address this, herein we explore the use of dimensionality-reduction techniques such as, Single Value Decomposition (SVD) and Principal Component Analysis (PCA), which are widely employed, for similar purposes, in the domain of data science. The feasibility of such methods is investigated via impact-based flood maps derived by a detailed mechanistic simulator in real-world conditions.